most citedSampling-Based System Identification with Active Exploration for Legged Robot Sim2Real Learning

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cs.RO2025

Hold My Beer: Learning Gentle Humanoid Locomotion and End-Effector Stabilization Control

Yitang Li, Yuanhang Zhang, Wenli Xiao +5

Can your humanoid walk up and hand you a full cup of beer, without spilling a drop? While humanoids are increasingly featured in flashy demos like dancing, delivering packages, tra…

cs.RO20251 cited

Sampling-Based System Identification with Active Exploration for Legged Robot Sim2Real Learning

Nikhil Sobanbabu, Guanqi He, Tairan He +2

Sim-to-real discrepancies hinder learning-based policies from achieving high-precision tasks in the real world. While Domain Randomization (DR) is commonly used to bridge this gap,…

cs.RO20251 cited

Flying Hand: End-Effector-Centric Framework for Versatile Aerial Manipulation Teleoperation and Policy Learning

Guanqi He, Xiaofeng Guo, Luyi Tang +6

Aerial manipulation has recently attracted increasing interest from both industry and academia. Previous approaches have demonstrated success in various specific tasks. However, th…

cs.RO2025

ASAP: Aligning Simulation and Real-World Physics for Learning Agile Humanoid Whole-Body Skills

Tairan He, Jiawei Gao, Wenli Xiao +15

Humanoid robots hold the potential for unparalleled versatility in performing human-like, whole-body skills. However, achieving agile and coordinated whole-body motions remains a s…

cs.RO2024

Self-Supervised Meta-Learning for All-Layer DNN-Based Adaptive Control with Stability Guarantees

Guanqi He, Yogita Choudhary, Guanya Shi

A critical goal of adaptive control is enabling robots to rapidly adapt in dynamic environments. Recent studies have developed a meta-learning-based adaptive control scheme, which…